Abstract
This paper presents a memetic algorithm (MA) to minimize the total weighted number of late jobs (or deliveries) on a single machine. The proposed MA combines a genetic algorithm (GA) with a neighborhood search. The performance of the proposed algorithm is compared with four heuristics from the literature, namely the early due date (EDD), the weighted shortest processing time (WSPT), the forward algorithm (FA), and the weighted forward algorithm (WFA), against 10 benchmark problems and three real-world problems. The results suggest that the MA outperformed the EDD, WSPT, FA, and WFA on the benchmark problems and performed as good as WFA on two of the three real-world problems and outperformed WFA on one real-world problem. The EDD performed the worst among the five solution approaches.
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